Photonic kernel machines for ultrafast spectral analysis

We present photonic kernel machines, a machine learning-inspired scheme for ultrafast spectral analysis of noisy radio-frequency signals from single-shot optical intensity measurements that harnesses fast photonic hardware to reach throughput rates ideally well above the gigahertz. We first theoretically describe some of their key underlying principles and then numerically illustrate their performance on a photonic lattice-based implementation. We apply this model both to picosecond pulsed signals, on an energy-spectral-density estimation and a shape classification tasks, and to continuous signals, on a frequency tracking task. The presented optical-computing scheme proves robust to noise while requiring minimal control on the photonic-lattice parameters, thus making it readily implementable in realistic state-of-the-art photonic platforms.